Pandemie Dialog (PD): data and analysis migration

0.1 Setup

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# define alternative package directory
r_on_cluster <- FALSE
if (r_on_cluster == TRUE) {
  bp <- "/???"
  computer <- "???"
  .libPaths(
    paste0(
      bp,
      "???",
      computer
    )
  )
} else {
  if (grepl(x = getwd(), pattern =  "carl")) {
    bp <- "/home/carl/Dokumente/06_projects/"
    
    # set a more recent R snapshot as source repo
    # r = getOption("repos") 
    # r["CRAN"] = "https://mran.microsoft.com/snapshot/2022-12-08"
    # options(repos = r)
    # rm(r)
  }
}

#+ load.packages, include=F
for (i in c(
  "renv",
  "data.table",
  "brms",
  "here",
  "broom",
  "jtools",
  "broom.mixed",
  "readxl",
  "Hmisc",
  "sparkline",
  "ggplot2",
  "scales",
  "ggrepel",
  "ggthemes",
  "corrplot",
  "cowplot",
  "plotly")
  ) {
  suppressPackageStartupMessages(
    library(i, character.only = TRUE
    ))}

# Check unsuccessful updates packages
# old.packages()

# Update packages to that snapshot
# update.packages(
#   ask = FALSE, 
#   checkBuilt = TRUE
# )

# ggplot theme
ggplot2::theme_set(
  theme_tufte(base_size = 14)  + 
    theme(panel.background = element_rect(colour = "grey35"),
          plot.background = element_rect(fill = "white", 
                                         colour = NA)
    )
)


# When within the Charite network or VPN, proxy settings need to be adapted
chariteVPN <- TRUE
if (chariteVPN) {
    Sys.setenv(https_proxy = "proxy.charite.de:8080")
    Sys.setenv(http_proxy = "proxy.charite.de:8080")
}


# Knitr should use the project root and not the script location as root
knitr::opts_knit$set(root.dir = here())
knitr::opts_knit$set(base.dir = here("scripts"))
print(here())

[1] "/home/carl/Dokumente/06_projects/2304_covid_testingSESconstraints"

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# Give data.table enough threads
writeLines(paste0("Threads available: ", parallel::detectCores()))

Threads available: 16

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writeLines(paste0("Threads given to data.table: ", parallel::detectCores() / 2))

Threads given to data.table: 8

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setDTthreads(parallel::detectCores() / 2)

# Option setup for
options(prType = 'html')
options(knitr.table.format = "html")
# options(grType = 'plotly')

0.2 Load raw data

I received the raw data for the analysis. No explanation or anything is given for any of the files.

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data_1 <- fread(here("data/raw-data/owid-covid-data_220208_22c8de6.txt"))
data_2 <- fread(here("data/raw-data/data2.txt"))
data_3 <- read_xlsx(here("data/raw-data/global burden disease region.xlsx")) |>
  as.data.table()

Three files are available for analysis:

• data/raw-data/data2.txt: Used for the main analysis
• data/raw-data/global burden disease region.xlsx: Gives countries for global regions used in the paper.
• data/raw-data/owid-covid-data_220208_22c8de6.txt: Maybe the raw data

0.2.1 Explore the raw data

Find some information about the first data and use it to annotate or name it. It looks like only the file data/raw-data/data2.txt was used in the analysis.

Open Questions:

• When was the data accessed?
• Several variables have typos:
  • data_2: “Vccination21”

Check on basic descriptive statistics like missingness.

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# Hmisc function to quickly scan for NAs in data
contents(data_1)

---

Data frame:data_1

160237 observations and 67 variables, maximum # NAs:155098  

---

Name	Class	Storage	NAs
iso_code		character	0
continent		character	0
location		character	0
date	IDate	integer	0
total_cases		double	2875
new_cases		double	2909
new_cases_smoothed		double	4060
total_deaths		double	20477
new_deaths		double	20310
new_deaths_smoothed		double	20440
total_cases_per_million		double	3607
new_cases_per_million		double	3641
new_cases_smoothed_per_million		double	4787
total_deaths_per_million		double	21196
new_deaths_per_million		double	21029
new_deaths_smoothed_per_million		double	21159
reproduction_rate		double	39346
icu_patients		double	137694
icu_patients_per_million		double	137694
hosp_patients		double	136956
hosp_patients_per_million		double	136956
weekly_icu_admissions		double	155098
weekly_icu_admissions_per_million		double	155098
weekly_hosp_admissions		double	149821
weekly_hosp_admissions_per_million		double	149821
new_tests		double	94096
total_tests		double	92909
total_tests_per_thousand		double	92909
new_tests_per_thousand		double	94096
new_tests_smoothed		double	78677
new_tests_smoothed_per_thousand		double	78677
positive_rate		double	83915
tests_per_case		double	84475
tests_units		character	0
total_vaccinations		double	117442
people_vaccinated		double	119464
people_fully_vaccinated		double	122214
total_boosters		double	145084
new_vaccinations		double	124688
new_vaccinations_smoothed		double	81607
total_vaccinations_per_hundred		double	117442
people_vaccinated_per_hundred		double	119464
people_fully_vaccinated_per_hundred		double	122214
total_boosters_per_hundred		double	145084
new_vaccinations_smoothed_per_million		double	81607
new_people_vaccinated_smoothed		double	82782
new_people_vaccinated_smoothed_per_hundred		double	82782
stringency_index		double	34430
population		double	1049
population_density		double	17606
median_age		double	27311
aged_65_older		double	28753
aged_70_older		double	28024
gdp_per_capita		double	26692
extreme_poverty		double	72237
cardiovasc_death_rate		double	28338
diabetes_prevalence		double	21427
female_smokers		double	57948
male_smokers		double	59416
handwashing_facilities		double	94165
hospital_beds_per_thousand		double	40958
life_expectancy		double	10666
human_development_index		double	28863
excess_mortality_cumulative_absolute		double	154777
excess_mortality_cumulative		double	154777
excess_mortality		double	154777
excess_mortality_cumulative_per_million		double	154777

---

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contents(data_2)

---

Data frame:data_2

109 observations and 41 variables, maximum # NAs:78  

---

Name	Storage	NAs
location	character	0
GBDR7	character	0
Testing20	double	0
Incidence20	double	0
Mortality20	double	0
Vaccination20	double	78
Testing21	double	0
Incidence21	double	0
Mortality21	double	0
Vccination21	double	0
max20_21_tests	double	0
max20_21_cases	double	0
max20_21_deaths	double	0
max20_21_peopleVaccinated	double	0
Age	double	0
GE	double	0
VA	double	0
PS	double	0
RQ	double	0
RL	double	0
CC	double	0
LEB	double	0
EYS	double	0
MYS	double	0
GNI	double	0
Gini	double	0
Urban	double	0
CHE	double	0
MMR	integer	0
FLB	double	0
ABR	double	0
SSP	double	0
PSE	double	0
EP	double	0
VE	double	0
Population	integer	0
PD	double	0
AAG	double	0
GDP	double	0
GI	double	0
HDI	double	0

---

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contents(data_3)

---

Data frame:data_3

113 observations and 5 variables, maximum # NAs:6  

---

Name	Storage	NAs
Country.Territory	character	0
Code	character	0
Region	character	0
GBDR 21	character	6
GBDR7	character	0

---

This describe() call would be nice but currently the rendering fails when loading qreport package.

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d <- describe(data_2)
html(d, size = 80, scroll = TRUE)

Summaries can also quickly be created using the movStats() function

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# A shorthand for calculating summaries using data.table
writeLines("Number of tests 2020 per meta-region:")

Number of tests 2020 per meta-region:

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movStats(Testing20 ~ GBDR7,
         discrete = TRUE,
         data = data_2)

GBDR7	Mean	Median	Q1	Q3	N
Central Europe, Eastern Europe, and Central Asia	30294.771	24968.269	18129.900	41001.340	22
High-Income	61784.670	47192.225	37005.739	68743.127	31
Latin America and Caribbean	8421.696	6527.192	3807.623	10444.780	16
North Africa and Middle East	53796.457	29956.639	16143.509	39585.406	10
Southeast Asia, East Asia, and Oceania	9660.020	4544.116	2358.742	7970.958	12
Sub-Saharan Africa	3775.411	1754.962	1074.871	2931.400	18

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writeLines("Number of tests 2021 per meta-region:")

Number of tests 2021 per meta-region:

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movStats(Testing21 ~ GBDR7,
         discrete = TRUE,
         data = data_2, )

GBDR7	Mean	Median	Q1	Q3	N
Central Europe, Eastern Europe, and Central Asia	142091.94	69717.717	56055.562	166248.05	22
High-Income	283991.57	153484.133	107843.222	228270.25	31
Latin America and Caribbean	21841.70	18208.845	8218.167	25124.61	16
North Africa and Middle East	175106.77	80517.537	43687.407	110572.69	10
Southeast Asia, East Asia, and Oceania	50865.25	22274.571	9248.634	39619.76	12
Sub-Saharan Africa	10733.52	3711.001	2602.542	11817.16	18

Check the data sources. data_2 contains per-country information on testing, indidences, mortality etc.

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# Check number of locations
writeLines("Total number of countries:")

Total number of countries:

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data_2[, uniqueN(location)]

[1] 109

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# Number of entries per location
writeLines("Countries per meta-region:")

Countries per meta-region:

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data_2[, .N, GBDR7]

GBDR7	N
Central Europe, Eastern Europe, and Central Asia	22
High-Income	31
Latin America and Caribbean	16
North Africa and Middle East	10
Southeast Asia, East Asia, and Oceania	12
Sub-Saharan Africa	18

Create a long-form version of the data and print summary information.

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# long format helps evaluate each variable
data_2_long <- melt(data_2, id.vars = c("location", "GBDR7")) |> 
  suppressWarnings()

writeLines("Variables and missings per meta-region:")

Variables and missings per meta-region:

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data_2_long[,.(
  total_variables = uniqueN(variable),
  total_locations = uniqueN(location),
 total_NA = sum(is.na(value)),
 relative_NA = signif(sum(is.na(value))/.N, 2)
), GBDR7]

GBDR7	total_variables	total_locations	total_NA	relative_NA
Central Europe, Eastern Europe, and Central Asia	39	22	11	0.013
High-Income	39	31	14	0.012
Latin America and Caribbean	39	16	14	0.022
North Africa and Middle East	39	10	9	0.023
Southeast Asia, East Asia, and Oceania	39	12	12	0.026
Sub-Saharan Africa	39	18	18	0.026

We also have longitudinal data available in data_1.

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writeLines("Variables and missings per meta-region (top 5):")

Variables and missings per meta-region (top 5):

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data_1[, .(measurements = uniqueN(date)), .(continent, location)][order(-measurements)] |> 
  head()

continent	location	measurements
South America	Argentina	769
North America	Mexico	769
South America	Peru	769
Asia	Thailand	766
Asia	Taiwan	754
	Asia	748

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# this is the classical longitudinal covid data
data_1[continent == "", .N, location]

location	N
Africa	726
Asia	748
Europe	747
European Union	747
High income	748
International	732
Low income	716
Lower middle income	748
North America	748
Oceania	745
South America	717
Upper middle income	748
World	748

This is a nice new method to plot categorical information.

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# separately for each year to scan for differences
plot(summaryM(Mortality20 + Testing20 + Incidence20 + Vaccination20 ~ GBDR7, 
              data = data_2))

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plot(summaryM(Mortality21 + Testing21 + Incidence21 + Vccination21 ~ GBDR7,
              data = data_2))

0.3 Exploratory plots

We have several indices, some are a combination of others, like the HDI or GI.

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all_indices <- c(
  "HDI",
  "LEB",
  "EYS",
  "MYS",
  "GNI",
  "GDP",
  "CHE",
  "GI",
  "MMR",
  "ABR",
  "FLB",
  "PSE",
  "Gini",
  "UP",
  "PD",
  "EP",
  "VE",
  "AAG"
)

# I want the column names to match the proper index names
all_indices[!(all_indices %in% colnames(data_2))]

[1] "UP"

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# Correct typo in name
setnames(data_2, "Vccination21", "Vaccination21")

# UP
setnames(x = data_2, "Urban", "UP")

# I want to remember what indices are for what and how they are connected
indices_structured <- list(
  Wealth = list("HDI" = c("LEB", "EYS", "MYS", "GNI"), 
                "GDP" = c(), 
                "CHE" = c()
                ),
  Inequality = list("GI" = c("MMR", "ABR", "FLB", "PSE"),
                    "Gini" = c()
                    ),
  Demographic_SES = list("UP" = c(),
                         "PD" = c(),
                         "EP" = c(),
                         "VE" = c()
                         ),
  Governance = list("AAG" = c()),
  "Mortality" = c(),
  "Incidence" = c(),
  "Vaccination" = c(),
  "Test_Capacity" = c()
)

0.3.1 Index correlation

I can plot basic overviews of the predictor data. First plot correlation of all variables.

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#|fig-width: 8
#|fig-height: 8

tmp <- data_2[, cor(.SD, method = "spearman"), .SDcols = c(all_indices)]
corrplot(tmp, addCoef.col = "black", number.cex = .7)

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tiff(
  filename = "paper/figures/indices_spearman_corrplot.tiff", 
  height = 8, 
  width = 8,
  res = 150, 
  units = "in"
  )
corrplot(tmp, addCoef.col = "black", number.cex = .7)
dev.off()

png 
  2 

Plot correlation of indices HDI & GI and their respective sub-indices.

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#|fig-width: 5
#|fig-height: 5

for (i in list(
              c("HDI", indices_structured$Wealth$HDI),
              c("GI", indices_structured$Inequality$GI)
              )
) {
  tmp <- data_2[, cor(.SD,method = "spearman"), .SDcols = i]
  corrplot(tmp, addCoef.col = "black")
}

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# Clean up
rm(tmp)

data_2$log_GDP <- log10(data_2$GDP)
data_2$log_PD <- log10(data_2$PD)
data_2$log_GNI <- log10(data_2$GNI)
data_2$log_MMR <- log10(data_2$MMR)

# all indices with log
all_log_indices <- c("log_GDP", "log_PD", "log_GNI", "log_MMR", 
                     all_indices[!(all_indices %in% c("GDP", "PD", "GNI", "MMR"))])

# Scatter plot of index correlation
data_2[, pairs(.SD), .SDcols = c("HDI", "LEB", "EYS", "MYS", "log_GNI")]

NULL

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data_2[, pairs(.SD), .SDcols = c("GI", "log_MMR", "ABR", "FLB", "PSE")]

NULL

0.3.2 Index histograms

Basic histograms of the predictors:

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par(mfrow = c(6,3))
options(grType = 'base')
for (i in seq_along(all_log_indices)) {
  
  # i <- 1
  hist(
    unlist(data_2[, .SD, .SDcols = all_log_indices[i]]),
    main = all_log_indices[i], 
    xlab = NULL, 
    ylab = NULL
    )
}

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options(grType = 'plotly')
par(mfrow = c(1,1))

Save plot.

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tiff(
  filename = "paper/figures/indices_hist.tiff", 
  height = 12, 
  width = 6,
  res = 150, 
  units = "in"
  )

par(mfrow = c(6,3))
options(grType = 'base')
for (i in seq_along(all_log_indices)) {
  
  # i <- 1
  hist(
    unlist(data_2[, .SD, .SDcols = all_log_indices[i]]),
    main = all_log_indices[i], 
    xlab = NULL, 
    ylab = NULL
    )
}


options(grType = 'plotly')
par(mfrow = c(1,1))
dev.off()

png 
  2 

Create data for plotting.

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# For plotting by year I need to melt the data by year
data_2_year <- melt(
  data_2, 
  id.vars =  c("location", "GBDR7"), 
  measure.vars = c("Testing20", "Testing21"), 
  variable.name = "Year", 
  value.name = "Tests"
)
data_2_year[, Year := ifelse(Year == "Testing20", "2020", "2021")]

# Get log of PD for plotting
data_2$log_PD <- log10(data_2$PD)

# select my interesting indices
nci <- c("CHE", "UP", "log_PD", "EP", "GI", "VE", "Gini")

# Add the indices
m1 <- match(data_2_year$location, data_2$location)
data_2_year[, c(nci, "Population") := 
              data_2[(m1), .SD, .SDcols = c((nci), "Population")]]

# Plot data with their mean
data_2_plot <- copy(data_2_year)

# Melt again to plot all indices at the same time
data_2_plot <- melt(data_2_plot, 
                    measure.vars = nci, 
                    variable.name = "Index", 
                    value.name = "Value", 
                    variable.factor = FALSE)

0.3.3 Scatter plot of pooled data

Scatter plot of TC with independent variables:

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# Simpsons Paradox 
# ggplot(data_2_plot[Index != "GDP"]) +
p <- ggplot(data_2_plot) +
  geom_point(aes(x = Value, 
                 y = Tests, 
                 col = GBDR7, 
                 size = Population)) + 
  facet_grid(Year ~ Index, scales = "free", 
             labeller = labeller(.cols = c(CHE = "CHE", 
                                           UP = "UP",
                                           log_PD = "log(PD)",
                                           EP = "EP",
                                           GI = "GI",
                                           Gini = "Gini",
                                           VE = "VE")
                                 )
             ) + 
  scale_y_continuous(
    trans = "log10",
    labels = trans_format("log10",
                          math_format(10^.x))) +
  annotation_logticks(sides = "l", 
                      outside = T, 
                      long = grid::unit(1.5, "mm"),
                      mid = grid::unit(1.1, "mm"),
                      short = grid::unit(.8, "mm")
                      ) +
  coord_cartesian(clip = "off") +
  labs(y = "Testing", 
       x = NULL) +
  guides(size = guide_legend()) +
  
  scale_size_continuous() + 
  
  theme(legend.position = "bottom", 
        legend.direction = "vertical",
        legend.justification = "left", 
        axis.ticks.y = element_blank(),
        panel.spacing = unit(1, "lines")
        ) +
  geom_smooth(method = "lm", 
              aes(x = Value, 
                  y = Tests, 
                  group = GBDR7, 
                  col =  GBDR7), 
              se = F,
              formula = "y ~ x")

print(p)

Save.

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ggsave(p, 
       filename = "tc_regional_pooled_scatter.tiff",
       path = "paper/figures/",
       device = "tiff", 
       width = 12, 
       height = 6, 
       dpi = 150)

0.3.4 Scatter plot per region

Per-region display to illustrate the effect difference within the regions.

1 TODO Add vaccination plot

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# Simpsons Paradox 
plot_list <- vector("list", length(nci))
names(plot_list) <- nci

for (i in seq_along(names(plot_list))) {
  
  index <- names(plot_list)[i]
  
  # i <- "GI"
  p <- ggplot(data_2_plot[Index == (index)], 
         aes(x = Value, 
             y = Tests, 
             col = GBDR7, 
             size = Population)
  ) +
    geom_point() + 
    facet_grid(Year ~ GBDR7) +
    scale_y_continuous(
      trans = "log10",
      labels = trans_format("log10",
                            math_format(10^.x))) +
    annotation_logticks(sides = "l", 
                        outside = T, 
                        long = grid::unit(1.5, "mm"),
                        mid = grid::unit(0, "mm"),
                        short = grid::unit(0, "mm")
    ) +
    coord_cartesian(clip = "off") +
    labs(y = "Testing", x = index) +
    guides(size = "none") + 
    theme(legend.position = "bottom", 
          legend.direction = "vertical",
          legend.justification = "left", 
          axis.ticks.y = element_blank(),
          strip.text.x = element_blank()) +
  geom_smooth(method = "lm", 
              aes(x = Value, 
                  y = Tests, group = GBDR7), 
              col = "indianred4",
              formula = "y ~ x")
  
  # Remove all but the last legend for plotting
  if (i != length(names(plot_list))) {
    
    p <- p + theme(legend.position = "none")
    
  }
  
  # Add to list
  plot_list[[index]] <- p
  
}

# Extract the color legend to plot separately
legend <- get_legend(
  # create some space to the left of the legend
  last(plot_list)
)

# Remove from the plot to only plot extracted 
plot_list[[7]] <- plot_list[[7]] + theme(legend.position = "none")
plot_list[["Legend"]] <- legend

# Plotting
cowplot::plot_grid(plotlist = plot_list, ncol = 1)

Save

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ggsave(cowplot::plot_grid(plotlist = plot_list, ncol = 1) + 
         theme(plot.background = element_rect(fill = "white", 
                                              color = NA)), 
       filename = "tc_regional_separate_scatter.tiff",
       path = "paper/figures/",
       device = "tiff", 
       width = 8, 
       height = 20, 
       dpi = 150)

1.0.1 Boxplot of testing for each year

Testing increase between 2020 & 2021

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p1 <- ggplot(data_2_year, 
       aes(y = Tests,
           x = GBDR7, 
           fill = Year)
       ) +
  geom_boxplot() +
  annotation_logticks(sides = "b",
                      outside = TRUE) +
  theme(axis.ticks.x = element_blank()) +
  scale_y_log10(
    breaks = scales::trans_breaks("log10", function(x) 10^x),
    labels = scales::trans_format("log10", scales::math_format(10^.x))
  ) +
  coord_flip(clip = "off") +
  theme(axis.title.y = element_blank(),
        legend.position = "bottom")

1.0.2 Boxplot of Vaccination and Mortality for each year

For Vaccination

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# For plotting by year I need to melt the data by year
data_2_vacc <- melt(
  data_2, 
  id.vars =  c("location", "GBDR7"), 
  measure.vars = c("Vaccination20", "Vaccination21"), 
  variable.name = "Year", 
  value.name = "Vaccination"
)
data_2_vacc[, Year := ifelse(Year == "Vaccination20", "2020", "2021")]

p2 <- ggplot(data_2_vacc[Year == "2021"], 
       aes(y = Vaccination,
           x = GBDR7, 
           fill = Year)
       ) +
  geom_boxplot() +
  scale_fill_manual(values = scales::hue_pal()(2)[2]) +
  annotation_logticks(sides = "b",
                      outside = TRUE) +
  theme(axis.ticks.x = element_blank()) +
  scale_y_log10(
    breaks = scales::trans_breaks("log10", function(x) 10^x),
    labels = scales::trans_format("log10", scales::math_format(10^.x))
  ) +
  coord_flip(clip = "off") +
  theme(axis.title.y = element_blank(),
        legend.position = "bottom")

For Mortality

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# For plotting by year I need to melt the data by year
data_2_mort <- melt(
  data_2, 
  id.vars =  c("location", "GBDR7"), 
  measure.vars = c("Mortality20", "Mortality21"), 
  variable.name = "Year", 
  value.name = "Mortality"
)
data_2_mort[, Year := ifelse(Year == "Mortality20", "2020", "2021")]

p3 <- ggplot(data_2_mort, 
       aes(y = Mortality,
           x = GBDR7, 
           fill = Year)
       ) +
  geom_boxplot() +
  annotation_logticks(sides = "b",
                      outside = TRUE) +
  theme(axis.ticks.x = element_blank()) +
  scale_y_log10(
    breaks = scales::trans_breaks("log10", function(x) 10^x),
    labels = scales::trans_format("log10", scales::math_format(10^.x))
  ) +
  coord_flip(clip = "off") +
  theme(axis.title.y = element_blank(),
        legend.position = "bottom")

Display as cowplot

Show the code

legend <- get_legend(p1)
plot_list <- list(p1 + theme(legend.position = "none"), 
                  p3 + theme(legend.position = "none"),
                  p2 + theme(legend.position = "none"),
                  legend)

# Plotting
cp <- cowplot::plot_grid(plotlist = plot_list,
                   rel_heights = c(3,3,2,.6), 
                   ncol = 1, labels = c("A", "B", "C")) + 
         theme(plot.background = element_rect(fill = "white", 
                                              color = NA))

print(cp)

Save.

Show the code

ggsave(cp, 
       filename = "tc_vacc_mort_boxplot.tiff",
       path = "paper/figures/",
       device = "tiff", 
       width = 6, 
       height = 10, 
       dpi = 150)

1.1 New analysis - Testing

1.1.1 Rescale data

• PD outliers could become a problem –> use log10
• GI on a *10 scale makes the effect more interpretable (effect of changes +- 0.1 instead of +- 1)

Show the code

# Use log of PD
data_2$log_PD <- log10(data_2$PD)

# Rescale the GI to get the effect estimates in terms of 0.1 increase of GI
data_2$GI_10 <- data_2$GI * 10

# compare raw with scaled data
par(mfrow = c(2,2))
data_2[, plot(log2(Testing20) ~ PD, pch = 19)]

NULL

Show the code

data_2[, plot(log2(Testing20) ~ log_PD, pch = 19)]

NULL

Show the code

data_2[, hist(PD)]

$breaks
 [1]    0  200  400  600  800 1000 1200 1400 1600 1800 2000

$counts
 [1] 80 20  5  0  0  0  1  2  0  1

$density
 [1] 3.669725e-03 9.174312e-04 2.293578e-04 0.000000e+00 0.000000e+00
 [6] 0.000000e+00 4.587156e-05 9.174312e-05 0.000000e+00 4.587156e-05

$mids
 [1]  100  300  500  700  900 1100 1300 1500 1700 1900

$xname
[1] "PD"

$equidist
[1] TRUE

attr(,"class")
[1] "histogram"

Show the code

data_2[, hist(log_PD)]

$breaks
[1] 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

$counts
[1]  2  8 15 32 39  9  4

$density
[1] 0.03669725 0.14678899 0.27522936 0.58715596 0.71559633 0.16513761 0.07339450

$mids
[1] 0.25 0.75 1.25 1.75 2.25 2.75 3.25

$xname
[1] "log_PD"

$equidist
[1] TRUE

attr(,"class")
[1] "histogram"

Show the code

par(mfrow = c(1,1))

# Rescale testing for overview, remove those later
data_2$log_Testing20 <- log2(data_2$Testing20)
data_2$log_Testing21 <- log2(data_2$Testing21)

data_2$log_Mortality20 <- log2(data_2$Mortality20)
data_2$log_Mortality21 <- log2(data_2$Mortality21)

Show the code

tiff(
  filename = "paper/figures/pd_transformation.tiff", 
  height = 5, 
  width = 5,
  res = 150, 
  units = "in"
  )
par(mfrow = c(2,2))
data_2[, plot(log2(Testing20) ~ PD, pch = 19)]

NULL

Show the code

data_2[, plot(log2(Testing20) ~ log_PD, pch = 19)]

NULL

Show the code

data_2[, hist(PD)]

$breaks
 [1]    0  200  400  600  800 1000 1200 1400 1600 1800 2000

$counts
 [1] 80 20  5  0  0  0  1  2  0  1

$density
 [1] 3.669725e-03 9.174312e-04 2.293578e-04 0.000000e+00 0.000000e+00
 [6] 0.000000e+00 4.587156e-05 9.174312e-05 0.000000e+00 4.587156e-05

$mids
 [1]  100  300  500  700  900 1100 1300 1500 1700 1900

$xname
[1] "PD"

$equidist
[1] TRUE

attr(,"class")
[1] "histogram"

Show the code

data_2[, hist(log_PD)]

$breaks
[1] 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

$counts
[1]  2  8 15 32 39  9  4

$density
[1] 0.03669725 0.14678899 0.27522936 0.58715596 0.71559633 0.16513761 0.07339450

$mids
[1] 0.25 0.75 1.25 1.75 2.25 2.75 3.25

$xname
[1] "log_PD"

$equidist
[1] TRUE

attr(,"class")
[1] "histogram"

Show the code

par(mfrow = c(1,1))
dev.off()

png 
  2 

1.1.2 Select predictors

Corrplot of all variables

Show the code

all_variables <- c("log_Testing20", "log_Testing21", "Vaccination21",
                   "log_Mortality20", "log_Mortality21",
                   "GI", "Gini", "HDI", "log_GDP", "CHE", 
                   "UP", "log_PD", "EP", "VE", "AAG")

# Select coefficients not highly correlated
tmp <- data_2[, cor(.SD, method = "spearman"), 
              .SDcols = (all_variables)]

# Display correlation structure of indices again
corrplot(tmp, 
         addCoef.col = "black", 
         number.cex = 1)

Show the code

tiff(
  filename = "paper/figures/all_variables_spearman_corrplot.tiff", 
  height = 8, 
  width = 8,
  res = 150, 
  units = "in"
  )
corrplot(tmp, addCoef.col = "black", number.cex = .7)
dev.off()

png 
  2 

Pairs plot of all variables

Show the code

# Check scatter plot of all index pairs
data_2[, pairs(.SD), 
       .SDcols = (all_variables)]

NULL

Show the code

tiff(
  filename = "paper/figures/all_variables_pairs.tiff", 
  height = 11, 
  width = 11,
  res = 150, 
  units = "in"
  )
data_2[, pairs(.SD, pch = 20, cex = .8, cex.labels = .7), 
       .SDcols = (all_variables)]

NULL

Show the code

dev.off()

png 
  2 

Show the code

data_2$log_Testing20 <- NULL
data_2$log_Testing21 <- NULL
data_2$log_Mortality20 <- NULL
data_2$log_Mortality21 <- NULL

Show only the selected indices.

Show the code

# select all vars with > 0.8 correlation
tmp_filter <- tmp |> 
  as.data.table(keep.rownames = TRUE) |> 
  melt.data.table(id.vars = "rn",
                  variable.factor = FALSE)
tmp_filter <- tmp_filter[rn != variable]

# Remove indices that correlate higly!
index_filter <- c("log_GDP", "HDI", "AAG", 
                  "log_Testing20",
                  "log_Testing21",
                  "Vaccination21",
                  "log_Mortality20",
                  "log_Mortality21")

# Only take the non-correlated indices
non_correlated_indices <- tmp_filter[!(rn %in% index_filter) & 
                                       !(variable %in% index_filter), 
                                     unique(c(rn, variable))]

# Display correlation of these indices
tmp_filter <- tmp_filter[!(rn %in% index_filter) & 
             !(variable %in% index_filter)] |> 
  dcast.data.table(rn ~ variable)
tmp_filter[, rn := NULL]
tmp_filter <- tmp_filter |> as.matrix()
rownames(tmp_filter) <- colnames(tmp_filter)
diag(tmp_filter) <- 1

Plotting

Show the code

corrplot(tmp_filter, 
         addCoef.col = "black", 
         number.cex = 1.3, tl.cex = 1.5, cl.cex = 1.5)

Show the code

# Display pairwise scatter plots again
data_2[, pairs(.SD, 
               pch = 20,
               cex = .8,
               cex.labels = 1), 
       .SDcols = non_correlated_indices]

NULL

Save plots

Show the code

tiff(
  filename = "paper/figures/main_variables_spearman_corrplot.tiff", 
  height = 8, 
  width = 8,
  res = 150, 
  units = "in"
  )
corrplot(tmp_filter, 
         addCoef.col = "black", 
         number.cex = 1.3, tl.cex = 1.5, cl.cex = 1.5)

dev.off()

png 
  2 

Show the code

tiff(
  filename = "paper/figures/main_variables_pairs.tiff", 
  height = 8, 
  width = 8,
  res = 150, 
  units = "in"
  )
data_2[, pairs(.SD, 
               pch = 20,
               cex = .8,
               cex.labels = 1), 
       .SDcols = non_correlated_indices]

NULL

Show the code

dev.off()

png 
  2 

1.2 Save data

Show the code

data_2$Testing20 <- as.integer(data_2$Testing20)
data_2$Testing21 <- as.integer(data_2$Testing21)

fwrite(data_2, file = "data/clean-data/owid-data.tsv", sep = "\t")

1.3 Old analysis

1.3.1 Supplemental Code

Show the code

####################################################################################
### 1. Preparing COVID-19 related data (Testing, mortality, incidence, vaccination)
####################################################################################

### Load Global Burden Disease Classification

library(readxl)
library(tidyr)
library(dplyr)


country_classification <- readxl::read_xlsx("data/raw-data/global burden disease region.xlsx")
country_classification$GBDR7 <- as.factor(country_classification$GBDR7)


### Load COVID-19 related data

OWID <- read.csv("data/raw-data/owid-covid-data_220208_22c8de6.txt", sep = ",")

### Clean COVID-19 related data

OWID$Month <- lubridate::ceiling_date(
  as.Date(OWID$date),
  unit = "month") - 1
OWID$Year <- format(OWID$Month,"%Y")
OWID_Monthly <- OWID %>%
  dplyr::group_by(
    iso_code,
    continent,
    location,
    Month,
    Year
  ) %>%
  dplyr::summarise(
    max_total_tests_per_Month = max(total_tests, na.rm = T),
    max_total_cases_per_Month = max(total_cases, na.rm = T),
    max_total_deaths_per_Month = max(total_deaths, na.rm = T),
    max_total_peopleVaccinated_per_Month = max(people_vaccinated, na.rm = T),
    population_InMonth = median(population),
    median_age_InMonth = median(median_age),
    population_density_InMonth = median(population_density)
  ) %>%
  arrange(iso_code, Month) %>%
  filter(Month < as.Date("2022-01-01")) %>%
  filter(Month >= as.Date("2020-03-01")) %>%
  ungroup()

# Remove -Inf induced by using max(na.rm = T)
OWID_Monthly$max_total_tests_per_Month[OWID_Monthly$max_total_tests_per_Month == -Inf] <- NA
OWID_Monthly$max_total_cases_per_Month[OWID_Monthly$max_total_cases_per_Month == -Inf] <- NA
OWID_Monthly$max_total_deaths_per_Month[OWID_Monthly$max_total_deaths_per_Month == -Inf] <- NA
OWID_Monthly$max_total_peopleVaccinated_per_Month[OWID_Monthly$max_total_peopleVaccinated_per_Month == -Inf] <- NA

# Fill Missing Months with NAs
OWID_MonthlyExtended <- OWID_Monthly %>% 
  complete(
    nesting(
      location,
      iso_code,
      continent),
    Month) 

# Find Months with missing Entries
OWID_MonthlyExtended$containsNA <- apply(
  OWID_MonthlyExtended[,c(
    "max_total_tests_per_Month",
    "max_total_cases_per_Month",
    "max_total_deaths_per_Month"
  )], 1, function(y) {
    any(is.na(y))
  }
)

# Countries with values for at least 50% of Months
countriesWithValidCaseTestDeathData <- OWID_MonthlyExtended %>%
  group_by(iso_code, continent, location) %>%
  summarise(missing = sum(containsNA), total = n()) %>%
  filter(missing / total < 0.5) %>%
  ungroup()

# Calculate Values per Month from cumulative Values for Countries matching the
# number of minimal required data points
OWID_explicit_Monthly <- OWID_Monthly %>%
  filter(iso_code %in% countriesWithValidCaseTestDeathData$iso_code)

# TODO: What happens here? Variable is not used
# List Countries with missing Region (External Data)
OWID_explicit_Monthly_Region_Missing_External_Data <- left_join(
  OWID_explicit_Monthly, 
  country_classification, 
  by = c("iso_code" = "Code")
  ) %>%
  filter(is.na(Region))

# Get monthly data for countries with Region
OWID_explicid_Monthly_Region <- left_join(
  OWID_explicit_Monthly, 
  country_classification, 
  by = c("iso_code" = "Code")
  ) %>%
  filter(!is.na(Region))

OWID_cumulative_Values_End2020 <- OWID_explicid_Monthly_Region %>%
  filter(Year == "2020") %>%
  group_by(iso_code) %>%
  summarise(
    max20_tests = max(max_total_tests_per_Month, na.rm = T),
    max20_cases = max(max_total_cases_per_Month, na.rm = T),
    max20_deaths = max(max_total_deaths_per_Month, na.rm = T),
    max20_peopleVaccinated = max(max_total_peopleVaccinated_per_Month, na.rm = T)
  ) %>%
  mutate(Year2substract = "2021")

OWID_cumulative_Values_End2020$max20_tests[OWID_cumulative_Values_End2020$max20_tests == -Inf] <- NA
OWID_cumulative_Values_End2020$max20_cases[OWID_cumulative_Values_End2020$max20_cases == -Inf] <- NA
OWID_cumulative_Values_End2020$max20_deaths[OWID_cumulative_Values_End2020$max20_deaths == -Inf] <- NA
OWID_cumulative_Values_End2020$max20_peopleVaccinated[OWID_cumulative_Values_End2020$max20_peopleVaccinated == -Inf] <- NA


OWID_cumulative_Values_End2021 <- OWID_explicid_Monthly_Region %>%
  filter(Year == "2021") %>%
  group_by(iso_code) %>%
  summarise(
    max20_21_tests = max(max_total_tests_per_Month, na.rm = T),
    max20_21_cases = max(max_total_cases_per_Month, na.rm = T),
    max20_21_deaths = max(max_total_deaths_per_Month, na.rm = T),
    max20_21_peopleVaccinated = max(max_total_peopleVaccinated_per_Month, na.rm = T)
  )

regionMeta <- OWID_explicid_Monthly_Region %>%
  select(
    iso_code, 
    location, 
    GBDR7, 
    population_InMonth, 
    median_age_InMonth,
    population_density_InMonth
  ) %>%
  distinct()

YearsMax <- full_join(
  OWID_cumulative_Values_End2020, 
  OWID_cumulative_Values_End2021, 
  by = c("iso_code" = "iso_code")
  ) %>%
  mutate(
    max21_tests = max20_21_tests - coalesce(max20_tests, 0),
    max21_cases = max20_21_cases - coalesce(max20_cases, 0),
    max21_deaths = max20_21_deaths - coalesce(max20_deaths, 0),
    max21_peopleVaccinated = max20_21_peopleVaccinated - coalesce(max20_peopleVaccinated, 0)
  ) %>%
  left_join(regionMeta, by = c("iso_code" = "iso_code")) %>%
  mutate(
    max21_tests_Per100k = max21_tests / population_InMonth * 100000,
    max21_cases_Per100k = max21_cases / population_InMonth * 100000,
    max21_deaths_Per100k = max21_deaths / population_InMonth * 100000,
    max21_peopleVaccinated_Per100k = max21_peopleVaccinated / population_InMonth * 100000,
    max20_tests_Per100k = max20_tests / population_InMonth * 100000,
    max20_cases_Per100k = max20_cases / population_InMonth * 100000,
    max20_deaths_Per100k = max20_deaths / population_InMonth * 100000,
    max20_peopleVaccinated_Per100k = max20_peopleVaccinated / population_InMonth * 100000,
  )




# Variable matching
setDT(YearsMax)
YearsMax[, .()] |> head()
data_2[order(location), .(location, Testing20, Testing21)] |> head()

1.4 Session Info

Show the code

sessioninfo::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.3.1 (2023-06-16)
 os       EndeavourOS
 system   x86_64, linux-gnu
 ui       X11
 language en_US.utf8
 collate  de_DE.UTF-8
 ctype    de_DE.UTF-8
 tz       Europe/Berlin
 date     2023-07-19
 pandoc   3.1.1 @ /usr/lib/rstudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package        * version date (UTC) lib source
 abind            1.4-5   2016-07-21 [1] CRAN (R 4.3.0)
 backports        1.4.1   2021-12-13 [1] CRAN (R 4.3.0)
 base64enc        0.1-3   2015-07-28 [1] CRAN (R 4.3.0)
 bayesplot        1.10.0  2022-11-16 [1] CRAN (R 4.3.0)
 bridgesampling   1.1-2   2021-04-16 [1] CRAN (R 4.3.0)
 brms           * 2.19.0  2023-03-14 [1] CRAN (R 4.3.0)
 Brobdingnag      1.2-9   2022-10-19 [1] CRAN (R 4.3.0)
 broom          * 1.0.5   2023-06-09 [1] CRAN (R 4.3.0)
 broom.mixed    * 0.2.9.4 2022-04-17 [1] CRAN (R 4.3.0)
 callr            3.7.3   2022-11-02 [1] CRAN (R 4.3.0)
 cellranger       1.1.0   2016-07-27 [1] CRAN (R 4.3.0)
 checkmate        2.2.0   2023-04-27 [1] CRAN (R 4.3.0)
 cli              3.6.1   2023-03-23 [1] CRAN (R 4.3.0)
 cluster          2.1.4   2022-08-22 [2] CRAN (R 4.3.1)
 coda             0.19-4  2020-09-30 [1] CRAN (R 4.3.0)
 codetools        0.2-19  2023-02-01 [2] CRAN (R 4.3.1)
 colorspace       2.1-0   2023-01-23 [1] CRAN (R 4.3.0)
 colourpicker     1.2.0   2022-10-28 [1] CRAN (R 4.3.0)
 corrplot       * 0.92    2021-11-18 [1] CRAN (R 4.3.0)
 cowplot        * 1.1.1   2020-12-30 [1] CRAN (R 4.3.0)
 crayon           1.5.2   2022-09-29 [1] CRAN (R 4.3.0)
 crosstalk        1.2.0   2021-11-04 [1] CRAN (R 4.3.0)
 data.table     * 1.14.8  2023-02-17 [1] CRAN (R 4.3.0)
 digest           0.6.31  2022-12-11 [1] CRAN (R 4.3.0)
 distributional   0.3.2   2023-03-22 [1] CRAN (R 4.3.0)
 dplyr            1.1.2   2023-04-20 [1] CRAN (R 4.3.0)
 DT               0.28    2023-05-18 [1] CRAN (R 4.3.0)
 dygraphs         1.1.1.6 2018-07-11 [1] CRAN (R 4.3.0)
 ellipsis         0.3.2   2021-04-29 [1] CRAN (R 4.3.0)
 evaluate         0.21    2023-05-05 [1] CRAN (R 4.3.0)
 fansi            1.0.4   2023-01-22 [1] CRAN (R 4.3.0)
 farver           2.1.1   2022-07-06 [1] CRAN (R 4.3.0)
 fastmap          1.1.1   2023-02-24 [1] CRAN (R 4.3.0)
 forcats          1.0.0   2023-01-29 [1] CRAN (R 4.3.0)
 foreign          0.8-84  2022-12-06 [2] CRAN (R 4.3.1)
 Formula          1.2-5   2023-02-24 [1] CRAN (R 4.3.0)
 furrr            0.3.1   2022-08-15 [1] CRAN (R 4.3.0)
 future           1.32.0  2023-03-07 [1] CRAN (R 4.3.0)
 generics         0.1.3   2022-07-05 [1] CRAN (R 4.3.0)
 ggplot2        * 3.4.2   2023-04-03 [1] CRAN (R 4.3.0)
 ggrepel        * 0.9.3   2023-02-03 [1] CRAN (R 4.3.0)
 ggthemes       * 4.2.4   2021-01-20 [1] CRAN (R 4.3.0)
 globals          0.16.2  2022-11-21 [1] CRAN (R 4.3.0)
 glue             1.6.2   2022-02-24 [1] CRAN (R 4.3.0)
 gridExtra        2.3     2017-09-09 [1] CRAN (R 4.3.0)
 gtable           0.3.3   2023-03-21 [1] CRAN (R 4.3.0)
 gtools           3.9.4   2022-11-27 [1] CRAN (R 4.3.0)
 here           * 1.0.1   2020-12-13 [1] CRAN (R 4.3.0)
 highr            0.10    2022-12-22 [1] CRAN (R 4.3.0)
 Hmisc          * 5.1-0   2023-05-08 [1] CRAN (R 4.3.0)
 htmlTable        2.4.1   2022-07-07 [1] CRAN (R 4.3.0)
 htmltools        0.5.5   2023-03-23 [1] CRAN (R 4.3.0)
 htmlwidgets      1.6.2   2023-03-17 [1] CRAN (R 4.3.0)
 httpuv           1.6.11  2023-05-11 [1] CRAN (R 4.3.0)
 httr             1.4.6   2023-05-08 [1] CRAN (R 4.3.0)
 igraph           1.5.0   2023-06-16 [1] CRAN (R 4.3.0)
 inline           0.3.19  2021-05-31 [1] CRAN (R 4.3.0)
 jsonlite         1.8.5   2023-06-05 [1] CRAN (R 4.3.0)
 jtools         * 2.2.1   2022-12-02 [1] CRAN (R 4.3.0)
 knitr            1.43    2023-05-25 [1] CRAN (R 4.3.0)
 labeling         0.4.2   2020-10-20 [1] CRAN (R 4.3.0)
 later            1.3.1   2023-05-02 [1] CRAN (R 4.3.0)
 lattice          0.21-8  2023-04-05 [2] CRAN (R 4.3.1)
 lazyeval         0.2.2   2019-03-15 [1] CRAN (R 4.3.0)
 lifecycle        1.0.3   2022-10-07 [1] CRAN (R 4.3.0)
 listenv          0.9.0   2022-12-16 [1] CRAN (R 4.3.0)
 loo              2.6.0   2023-03-31 [1] CRAN (R 4.3.0)
 magrittr         2.0.3   2022-03-30 [1] CRAN (R 4.3.0)
 markdown         1.7     2023-05-16 [1] CRAN (R 4.3.0)
 Matrix           1.5-4.1 2023-05-18 [2] CRAN (R 4.3.1)
 matrixStats      1.0.0   2023-06-02 [1] CRAN (R 4.3.0)
 mgcv             1.8-42  2023-03-02 [2] CRAN (R 4.3.1)
 mime             0.12    2021-09-28 [1] CRAN (R 4.3.0)
 miniUI           0.1.1.1 2018-05-18 [1] CRAN (R 4.3.0)
 munsell          0.5.0   2018-06-12 [1] CRAN (R 4.3.0)
 mvtnorm          1.2-2   2023-06-08 [1] CRAN (R 4.3.0)
 nlme             3.1-162 2023-01-31 [2] CRAN (R 4.3.1)
 nnet             7.3-19  2023-05-03 [1] CRAN (R 4.3.0)
 pander           0.6.5   2022-03-18 [1] CRAN (R 4.3.0)
 parallelly       1.36.0  2023-05-26 [1] CRAN (R 4.3.0)
 pillar           1.9.0   2023-03-22 [1] CRAN (R 4.3.0)
 pkgbuild         1.4.1   2023-06-14 [1] CRAN (R 4.3.0)
 pkgconfig        2.0.3   2019-09-22 [1] CRAN (R 4.3.0)
 plotly         * 4.10.2  2023-06-03 [1] CRAN (R 4.3.0)
 plyr             1.8.8   2022-11-11 [1] CRAN (R 4.3.0)
 posterior        1.4.1   2023-03-14 [1] CRAN (R 4.3.0)
 prettyunits      1.1.1   2020-01-24 [1] CRAN (R 4.3.0)
 processx         3.8.1   2023-04-18 [1] CRAN (R 4.3.0)
 promises         1.2.0.1 2021-02-11 [1] CRAN (R 4.3.0)
 ps               1.7.5   2023-04-18 [1] CRAN (R 4.3.0)
 purrr            1.0.1   2023-01-10 [1] CRAN (R 4.3.0)
 R6               2.5.1   2021-08-19 [1] CRAN (R 4.3.0)
 ragg             1.2.5   2023-01-12 [1] CRAN (R 4.3.1)
 Rcpp           * 1.0.10  2023-01-22 [1] CRAN (R 4.3.0)
 RcppParallel     5.1.7   2023-02-27 [1] CRAN (R 4.3.0)
 readxl         * 1.4.2   2023-02-09 [1] CRAN (R 4.3.0)
 renv           * 0.17.3  2023-04-06 [1] CRAN (R 4.3.0)
 reshape2         1.4.4   2020-04-09 [1] CRAN (R 4.3.0)
 rlang            1.1.1   2023-04-28 [1] CRAN (R 4.3.0)
 rmarkdown        2.22    2023-06-01 [1] CRAN (R 4.3.0)
 rpart            4.1.19  2022-10-21 [2] CRAN (R 4.3.1)
 rprojroot        2.0.3   2022-04-02 [1] CRAN (R 4.3.0)
 rstan            2.21.8  2023-01-17 [1] CRAN (R 4.3.0)
 rstantools       2.3.1   2023-03-30 [1] CRAN (R 4.3.0)
 rstudioapi       0.14    2022-08-22 [1] CRAN (R 4.3.0)
 scales         * 1.2.1   2022-08-20 [1] CRAN (R 4.3.0)
 sessioninfo      1.2.2   2021-12-06 [1] CRAN (R 4.3.0)
 shiny            1.7.4   2022-12-15 [1] CRAN (R 4.3.0)
 shinyjs          2.1.0   2021-12-23 [1] CRAN (R 4.3.0)
 shinystan        2.6.0   2022-03-03 [1] CRAN (R 4.3.0)
 shinythemes      1.2.0   2021-01-25 [1] CRAN (R 4.3.0)
 sparkline      * 2.0     2016-11-12 [1] CRAN (R 4.3.0)
 StanHeaders      2.26.27 2023-06-14 [1] CRAN (R 4.3.0)
 stringi          1.7.12  2023-01-11 [1] CRAN (R 4.3.1)
 stringr          1.5.0   2022-12-02 [1] CRAN (R 4.3.0)
 systemfonts      1.0.4   2022-02-11 [1] CRAN (R 4.3.0)
 tensorA          0.36.2  2020-11-19 [1] CRAN (R 4.3.0)
 textshaping      0.3.6   2021-10-13 [1] CRAN (R 4.3.1)
 threejs          0.3.3   2020-01-21 [1] CRAN (R 4.3.0)
 tibble           3.2.1   2023-03-20 [1] CRAN (R 4.3.0)
 tidyr            1.3.0   2023-01-24 [1] CRAN (R 4.3.0)
 tidyselect       1.2.0   2022-10-10 [1] CRAN (R 4.3.0)
 utf8             1.2.3   2023-01-31 [1] CRAN (R 4.3.0)
 vctrs            0.6.3   2023-06-14 [1] CRAN (R 4.3.0)
 viridisLite      0.4.2   2023-05-02 [1] CRAN (R 4.3.0)
 withr            2.5.0   2022-03-03 [1] CRAN (R 4.3.0)
 xfun             0.39    2023-04-20 [1] CRAN (R 4.3.0)
 xtable           1.8-4   2019-04-21 [1] CRAN (R 4.3.0)
 xts              0.13.1  2023-04-16 [1] CRAN (R 4.3.0)
 yaml             2.3.7   2023-01-23 [1] CRAN (R 4.3.0)
 zoo              1.8-12  2023-04-13 [1] CRAN (R 4.3.0)

 [1] /home/carl/Dokumente/01_programs/rpackages
 [2] /usr/lib/R/library

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